{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [基本分类：对服装图像进行分类](https://www.tensorflow.org/tutorials/keras/classification?hl=zh-cn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本指南使用 [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) 数据集，该数据集包含 10 个类别的 70,000 个灰度图像。这些图像以低分辨率（28x28 像素）展示了单件衣物，如下所示：\n",
    "\n",
    "![](./fashion-mnist.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (10000, 28, 28) <class 'numpy.ndarray'> uint8\n"
     ]
    }
   ],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "print(train_images.shape, test_images.shape, type(train_images), train_images.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 6s 2ms/step - loss: 0.4959 - accuracy: 0.8258\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3754 - accuracy: 0.8648\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.3386 - accuracy: 0.8762\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3129 - accuracy: 0.8852\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2927 - accuracy: 0.8913\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2788 - accuracy: 0.8966\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2658 - accuracy: 0.9018\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2557 - accuracy: 0.9050\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2458 - accuracy: 0.9081\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2389 - accuracy: 0.9115\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f1f74055d30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.3424 - accuracy: 0.8808\n",
      "\n",
      "Test accuracy: 0.8808000087738037\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.0364574e-09, 3.2065786e-10, 2.6196154e-10, 1.8599598e-11,\n",
       "       1.4495263e-09, 1.8786803e-05, 5.7819096e-09, 1.2193980e-03,\n",
       "       8.2021696e-09, 9.9876177e-01], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    true_label, img = true_label[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "\n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    true_label = true_label[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(10))\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0, 1])\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "    plot_image(i, predictions[i], test_labels, test_images)\n",
    "    plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "    plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.4318915e-06 1.3225722e-14 9.9968612e-01 2.4039387e-11 1.6231318e-04\n",
      "  1.7084138e-11 1.4519691e-04 1.6348582e-24 4.5467527e-10 3.6862495e-16]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
